Abstract
In this paper, the new results obtained with the development of a novel thermodynamic equilibrium-inspired optimization algorithm are presented. This technique was developed in order to solve nonlinear optimization problems, with continuous domains. In our proposal, each variable is considered as the most volatile chemical component of a saturated binary liquid mixture, at a determined pressure and temperature. In the search process, the new value of each decision variable is obtained at some temperature of bubble or dew of the binary system. The search includes the random change of the chemical species and their compositions. The algorithm has being tested by using well-known mathematical functions as benchmark functions and has given competitive results in comparison with other metaheuristics.
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Acknowledgements
The authors would like to thank the grants given as follows: PhD. Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR/1171243. PhD. Ricardo Soto is supported by grant CONICYT/FONDECYT/REGULAR/1160455. MSc. Enrique Cortés is supported by grant INF-PUCV 2015.
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Crawford, B., Soto, R., Cortés, E. (2018). New Advances in the Development of a Thermodynamic Equilibrium-Inspired Metaheuristic. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_12
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